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add export.py
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@ -19,6 +19,39 @@
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# This script converts several saved checkpoints
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# to a single one using model averaging.
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"""
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Usage:
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(1) Export to torchscript model using torch.jit.script()
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./rnn_lm/export.py \
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--exp-dir ./rnn_lm/exp \
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--epoch 20 \
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--avg 10 \
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--jit 1
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It will generate a file `cpu_jit.pt` in the given `exp_dir`. You can later
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load it by `torch.jit.load("cpu_jit.pt")`.
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Note `cpu` in the name `cpu_jit.pt` means the parameters when loaded into Python
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are on CPU. You can use `to("cuda")` to move them to a CUDA device.
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Check
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https://github.com/k2-fsa/sherpa
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for how to use the exported models outside of icefall.
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(2) Export `model.state_dict()`
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./rnn_lm/export.py \
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--exp-dir ./rnn_lm/exp \
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--bpe-model data/lang_bpe_500/bpe.model \
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--epoch 20 \
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--avg 10
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It will generate a file `pretrained.pt` in the given `exp_dir`. You can later
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load it by `icefall.checkpoint.load_checkpoint()`.
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"""
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import argparse
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import logging
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@ -83,7 +116,7 @@ def get_parser():
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parser.add_argument(
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"--exp-dir",
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type=str,
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default="rnnlm/exp",
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default="rnn_lm/exp",
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help="""It specifies the directory where all training related
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files, e.g., checkpoints, log, etc, are saved
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""",
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